Wearable Device-Aware Supervised Power Management for Mobile Platforms

ABSTRACT

Methods, systems, and computer program products are provided for supervised power management between a primary platform and a secondary platform. Communication between a primary platform and a secondary platform is established. An application running on the secondary platform is captured. Input features and output measures are collected to build a training set for the application, wherein the input features are collected through direct measurement and the output measures reflect characteristics of the application. Based on the training set, power consumption of the secondary platform with an expected performance level is predicted for a new application running on the secondary platform. Accordingly, an optimal power management policy is derived that minimizes the total power consumption of the primary and secondary platforms.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Provisional Application No.61/757,947, filed Jan. 29, 2013, entitled “POWER MANAGEMENT ON ABASEBAND CHIP,” and Provisional Application No. 61/877,851, filed Sep.13, 2013, entitled “WEARABLE DEVICE-AWARE SUPERVISED POWER MANAGEMENTFOR MOBILE PLATFORMS,” which are incorporated by reference herein intheir entireties.

BACKGROUND

1. Field of Disclosure

The present disclosure relates generally to power management techniques.

2. Related Art

The development of wearable technology is a response to the need ofubiquitous computing. Many companies have rolled out wearable devices,with functionalities ranging from sleep habit monitoring and caloriecounts, notifications for emails, phone calls and text messages, voiceactivation and facial recognition, games, to photo manipulation andsharing to social networks. Wearable devices constitute a secondarymobile platform that communicate seamlessly to the cloud via a primaryplatform in a short range.

The primary platform may be a smartphone or a tablet computer. Thesecondary platform may be a wearable device such as a smart watch, smartglass or other small wearable consumer electronics and embedded devices.Existing systems do not have a workload or environment-aware powermanagement policy for the secondary platform, especially to take intoconsideration that the primary and secondary platforms have differentbattery source and power management policies. Given that the behavior ofpower consumption of the secondary platform is frequently affected bythe primary platform, such conventional systems do not have coordinatedpower management between the primary and secondary platforms.Accordingly, they fail to optimize the primary and secondary powermanagement policies effectively to extend battery life to provide anideal user experience.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate embodiments of the present disclosureand, together with the description, further serve to explain theprinciples of the disclosure and to enable a person skilled in therelevant art to make and use the disclosure.

FIG. 1 illustrates an exemplary power consumption scheme between aprimary and secondary platform, according to an embodiment of thepresent disclosure.

FIG. 2 illustrates elements of an supervised power management system,according to an embodiment of the present disclosure.

FIG. 3 is a flowchart illustrating a method for supervised powermanagement, in accordance with an embodiment of the present disclosure.

FIG. 4 is a flowchart illustrating stages of the prediction of theupcoming power consumption and performance, in accordance with anembodiment of the present disclosure.

FIG. 5 depicts an example of collecting input features and outputmeasures, according to an embodiment of the present disclosure.

FIG. 6 depicts a classification of input features and output measures,according to another embodiment of the present disclosure.

FIG. 7 illustrates an example computer system 700 in which the presentdisclosure, or portions thereof, can be implemented as computer-readablecode.

The embodiments will be described in detail with reference to theaccompanying drawings. In the drawings, generally, like referencenumbers indicate identical or functionally similar elements.Additionally, generally, the left-most digit(s) of a reference numberidentifies the drawing in which the reference number first appears.

DETAILED DESCRIPTION I. Introduction

The following detailed description of the present disclosure refers tothe accompanying drawings that illustrate exemplary embodimentsconsistent with this disclosure. Other embodiments are possible, andmodifications can be made to the embodiments within the spirit and scopeof the disclosure. Therefore, the detailed description is not meant tobe limiting. Rather, the scope of the disclosure is defined by theappended claims.

As will be described in further detail below, embodiments can implementa coordinated power management policy that considers battery charge ordrain on both primary and secondary platforms, when determining DVFS(Dynamic Voltage Frequency Scaling) parameters for a specificapplication to be run on the secondary platform, such as a wearabledevice.

As will be described in further detail below, embodiments can generate atraining set based on the collection of the input features and outputmeasures of existing applications running on the secondary platform.Embodiments can further classify the training set into variouscategories representing a range of values for the output measures, suchas a power consumption level and data rate. Embodiments can provide atool to predict the optimal power management policy that strikes abalance between the trade-off of performance and power usage on bothplatforms. Embodiments can further adjust the DVFS parameters for a newapplication to be run on the secondary platform that minimizes powerconsumption on both platforms.

In one embodiment, the present disclosure relates to a system having amemory configured to store modules. The modules include a communicationmodule configured to establish communication between a primary platformand a secondary platform, an application capturing module configured tocapture an application running on the secondary platform, an collectingmodule configured to collect input features and output measures as atraining set for the application, wherein the input features arecollected through direct measurement and the output measures reflectcharacteristics of the application, and a power predicting moduleconfigured to predict power consumption of the secondary platform withan expected performance level for a new application running on thesecondary platform based on the training set. A processor, coupled tothe memory, is configured to process the modules.

According to a further embodiment of the disclosure, there is provided amethod including establishing communication between a primary platformand a secondary platform, capturing an application running on thesecondary platform, collecting input features and output measures as atraining set for the application, wherein the input features arecollected through direct measurement and the output measures reflectcharacteristics of the application, and predicting power consumption ofthe secondary platform with an expected performance level for a newapplication running on the secondary platform based on the training set.

Additional embodiments of the disclosure include a computer-readablestorage device having instructions stored thereon, execution of which,by a computing device, causes the computing device to perform operationscomprising establishing communication between a primary platform and asecondary platform, capturing an application running on the secondaryplatform, collecting input features and output measures as a trainingset for the application, wherein the input features are collectedthrough direct measurement and the output measures reflectcharacteristics of the application, and predicting power consumption ofthe secondary platform with an expected performance level for a newapplication running on the secondary platform based on the training set.

Reference to modules in this specification and the claims means anycombination of hardware, software, or firmware components for performingthe indicated function. A module need not be a rigidly defined entity,such that several modules may overlap hardware and software componentsin functionality. For example, a software module may refer to a singleline of code within a procedure, the procedure itself being a separatesoftware module. One skilled in the relevant arts will understand thatthe functionality of modules may be defined in accordance with a numberof stylistic or performance-optimizing techniques, for example.

Further features and advantages of the disclosure, as well as thestructure and operation of various embodiments of the disclosure, aredescribed in detail below with reference to the accompanying drawings.It is noted that the disclosure is not limited to the specificembodiments described herein. Such embodiments are presented herein forillustrative purposes only. Additional embodiments will be apparent topersons skilled in the relevant art(s) based on the teachings containedherein.

II. A Supervised Power Management System

FIG. 1 illustrates an exemplary power consumption scheme between aprimary and secondary platform, according to an embodiment of thepresent disclosure. FIG. 1 shows power consumption between a primaryplatform 102, and a secondary platform 104, running a 3D video playbackapplication. For example, primary platform 102 may be a smart phone or atablet computer. In one embodiment, secondary platform 104 may be awearable device such as a smart watch, smart glass or other consumerelectronics and embedded devices. Alternatively, secondary platform 104does not need to be a wearable device.

According to an embodiment, the wearable device on secondary platform104 communicates with primary platform 102 via short-ranged wirelesstechnologies such as Bluetooth, WirelessHD, Wireless Home DigitalInterface (WHDI), Wi-Fi, cellular, infrared, radio-frequencyidentification (RFID), near-field communication (NFC), ZigBee, Z-wareand etc. Primary platform 102 and secondary platform 104 may havedifferent battery sources and power management policies.

In one embodiment, in the event that no wearable device is detected inthe vicinity of primary platform 102, primary platform 102 implements alegacy or standalone power management policy. In another embodiment, ifthe wearable device is detected in the vicinity of primary platform 104,two power management policies for primary platform 102 and secondaryplatform 104 may be taken into consideration and a supervised powermanagement policy may be enabled. A supervised power management policymay control power management of secondary platform. For example, aDynamic Voltage and Frequency Scaling (DVFS) or power-gating mechanismmay be implemented for video/audio streaming on secondary platform 104.Alternatively, a supervised power management policy may adjust legacypower management policy on primary platform 102, such as implementing afeedback based control for primary platform 102. For example, if a useron secondary platform does not select HD video clips, primary platform102 may send video to secondary platform 104 in a relatively low framefrequency.

In the example of FIG. 1, upon detecting the presence of a wearabledevice, primary platform 102, such as a smart phone may enable thesupervised power management. In one embodiment, workload may be capturedand defined as a data rate ratio of transmission rate (Tx) and receivingrate (Rx) between primary platform 102 and secondary platform 104. Oneskilled in the relevant arts will appreciate that other parameters maybe used to define workload. In one embodiment, power consumption onsecondary platform 104 may be estimated with expected performance todecide an optimal power management policy between primary and secondaryplatforms 102 and 104. For example, a Dynamic Frequency Scaling (DFS)may be implemented to adjust the power consumption. Dynamic powerconsumption is a function of voltage and frequency. Thus, based on theexpected performance, the clock frequency on the secondary platform maybe changed. High frequency may correspond to high power consumption,

FIG. 1 shows the power consumption of a 3D video playback. Based on theframe size, there is an active period 106 which corresponds to highpower consumption and an idle period 108 which corresponds to low powerconsumption. Accordingly, adjusting the frame size may adjust the powerconsumption of a 3D video playback application running on secondaryplatform 104.

FIG. 2 illustrates elements of a supervised power management system,according to an embodiment of the present disclosure. In the exampleshown in FIG. 2, wearable device-aware supervised power managementsystem 200 includes a memory 210 and processors 220. Memory 210 furtherincludes a communication module 211, an application capturing module213, a collecting module 215, a power predicting module 217 and a policyadjusting module 219.

Communication module 211 establishes communication between a primaryplatform and a secondary platform, such as primary platform 102 andsecondary platform 104 illustrated in FIG. 1. As noted, the wearabledevice may establish communication with primary platform via short rangecommunications. In one embodiment, short range wireless communicationusing Bluetooth may be used to communicate between wireless headsets andaudio applications of the wearable device with the primary platform. Inanother embodiment, short range wireless communication using Wi-Fi maybe used to provide image compression between a wearable device with theprimary platform. In still another embodiment, short range wirelesscommunication using WirelessHD may be used to conduct video transferbetween the wearable device and the primary platform. In still anotherembodiment, short range wireless communication using Wireless HomeDigital Interface (WHDI) may be implemented between game applications ona wearable device communicating with the primary platform.

Application capturing module 213 captures an application running on thesecondary module, such as a 3D video playback running on the wearabledevice illustrated in FIG. 2. In an embodiment, capture an applicationindicates that an application running on the wearable device is detectedand a workload of the detected application is captured. For example,application capturing module 213 may capture workload defined as theTx/Rx data rate in the 3D video playback application running on thewearable device. Alternatively, application capturing module 213 maycapture a MP3 playback application on the wearable device. Stillalternatively, application capturing module 213 may capture a voiceactivation application on the wearable device. Still alternatively,application capturing module 213 may capture a web browser applicationon the wearable device.

Collecting module 215 collects input features and output measures as atraining set for the captured application, wherein the input featuresare collected through direct measurement and the output measures reflectcharacteristics of the captured application. The training set may beorganized as a training table as illustrated in FIG. 6.

In the example of a 31) playback application, the input features mayinclude a utilization rate, which is defined as the ratio of theactive_period over the total period. The total period is defined as theactive_period plus the idle_period. In another example of an imagingapplication running on a wearable device, the input features may includea frame frequency such as Frame Per Second (FPS).

In an embodiment, output measures may include power consumption on thesecondary platform. In another embodiment, output measures may includedata rate between the primary platform and the secondary platform.Accordingly, a training set may include parameters such as utilizationrate, frame frequency FPS, power consumption and data rate.

Power predicting module 217 predicts power consumption of the secondaryplatform with an expected performance level for a new applicationrunning on the secondary platform based on the training set. In anembodiment, power predicting module 217 predicts upcoming powerconsumption and performance level of the processor when a new task orapplication is executed on the secondary platform. In anotherembodiment, power predicting module 217 predicts the most likely outputlabel of the output measure given an input feature. Given the outputmeasures may within a range of values rather than a single value in thetraining table, the output class label indicates the class or categoryof values that an output measure belongs to. For example, an outputmeasure, such as data rate between the primary platform and the secondplatform, may be labeled as class D1, D2 and D3, with D1=[10 20] mbps,D2=[20 40] and D3=[40 60]. The output label will be further elaboratedin FIG. 6.

Optionally, power predicting module 217 may further include a trainingset building module configured to build the training set based on amachine learning technique.

Optionally, supervised power management system 200 may include a policyadjusting module 219 which adjusts a power management policy with anoptimal clock frequency of the secondary platform that minimizes a totalpower consumption of the primary platform and the secondary platform.

In an embodiment, the power management policy is adjusted based on:

$\begin{matrix}{{DFS}_{new} = {\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left( {{Energy}_{primary} + {Energy}_{secondary}} \right)}}} \\{= {\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left\lbrack {\left( \frac{{p_{-} \cdot t_{p}} + {p_{+} \cdot t_{p}}}{2} \right) + \left( \frac{{{func}\; {\left( d_{-} \right) \cdot t_{s}}} + {{{func}\left( d_{+} \right)} \cdot t_{s}}}{2} \right)} \right\rbrack}}}\end{matrix}$

such that FPS_(secondary)>user_defined

In the equation listed above, Energy_(primary) and Energy_(secondary)indicate the power consumption level of the primary and the secondplatform respectively. Thus,

$\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left( {{Energy}_{primary} + {Energy}_{secondary}} \right)}$

represents the minimal combined power consumption of the primary andsecondary platforms as a function of the clock frequencies freq 1 andfreq2, where freq 1 is a clock frequency of the primary platform; freq2is a clock frequency of the secondary platform; t_(p) is an activeperiod of the primary platform; t_(s) is an active period of thesecondary platform, assuming prediction of a power consumption rangePx=[p−, p+] and a data rate range Dy=[d−, d+] and the power consumptionof the secondary platform is estimated as P_(—)2^(nd)=[func(d−),func(d+)].

Optionally, supervised power management system 200 may include a featureclassification module configured to categorize a feature y of thesecondary platform into a class 1 based on:

$\begin{matrix}{{y_{MAP} = {\arg \; {\max\limits_{l}\mspace{11mu} {{Prob}\left( {{y = {lx_{1}}},x_{2},\ldots \mspace{14mu},x_{n}} \right)}}}}\;} \\{= {\arg \; {\max\limits_{l}\mspace{14mu} \frac{{Prob}\; {\left( {x_{1},x_{2},\ldots \mspace{14mu},{{x_{n}y} = l}} \right) \cdot {{Prob}\left( {y = l} \right)}}}{{Prob}\; \left( {x_{1},x_{2},\ldots \mspace{14mu},x_{n}} \right)}}}} \\{= {\arg \; {\max\limits_{l}\mspace{14mu} {{{Prob}\left( {y = l} \right)} \cdot {\prod\limits_{j = 1}^{l}\; {{prob}\left( {{x_{j}y} = l} \right)}}}}}}\end{matrix}$

wherein x1 is a utilization rate, x2 is a frame frequency of thesecondary platform, and y_(map) is a maximal probability of assigningclass 1 to a output feature y.

In an example of a variation, embodiments of the elements of supervisedpower management system 200 in FIG. 2, as described herein may befurther configured to run in parallel. Such parallel execution of theseelements would greatly increase the efficiency and speed of supervisedpower management system 200,

III. Methods

FIG. 3 is a flowchart illustrating a method for supervised powermanagement, in accordance with an embodiment of the present disclosure.For ease of explanation, method 300 will be described with respect tosystem 200 of FIG. 2, as described above. However, method 300 is notintended to be limited thereto.

At stage 302, communication between a primary platform and a secondaryplatform is established. For example, communication module 211establishes communication between a primary platform and a secondaryplatform. As noted, the communication may encompass short rangecommunication, such as Bluetooth, Wireless HD, WHDI, Wi-Fi, Cellular,Infrared, RFID, NFC, ZigBee, Z-ware and etc. In an embodiment, a syncprocess may occur between the primary and secondary platforms. Thesupervised power management may be enabled upon the completion of thesync process.

At stage 304, an application running on the secondary platform iscaptured. For example, application capturing module 213 captures anapplication running on the secondary platform. In an embodiment,workload of the captured application may be defined using appropriateparameters such as data rate between the primary and secondaryplatforms. In another embodiment, workload may affect the powerconsumption of the secondary platform, which depends on the primaryplatform as well.

In an embodiment, a user may select to view video clips from, forexample, a smart watch, where the video clips are located on the primaryplatform. The video playback may be enabled on the secondary platformand the video clips may be transferred from the primary platform to thesecondary platform. The applications running on the smart watch thatstream and display the video clips may be captured.

At stage 306, input features and output measures are collected as atraining set for the application, wherein the input features arecollected through direct measurement and the output measures reflectcharacteristics of the application. For example, collecting module 215collects input features and output measures as a training set for theapplication. FIG. 4 is a flowchart illustrating stages of the predictionof the upcoming power consumption and performance, in accordance with anembodiment of the present disclosure. Stage 306 may correspond toextraction period 402 illustrated in FIG. 4.

FIG. 5 depicts an example of collecting input features and outputmeasures, according to an embodiment of the present disclosure. In theexample illustrated in FIG. 5, input features 502 of a capturedapplication may include a utilization or utilization rate 506, which canbe defined as active_period/total_period, where total_period includesthe active_period and idle_period. For example, if utilization 506 is90%, an active period dominates, while an idle period dominates if theutilization 506 is 10%.

In an embodiment, input features 502 may include Frame Per Second (FPS)508. For example, in a video playback application, the FPS 508 may be 10or 60 FPS, while 60 FPS corresponds to high quality video playback.

In an embodiment, the output measurement 504 may include powerconsumption 510 of the secondary platform and data rate 512 between theprimary and secondary platforms. For example, a high 90% utilization 506corresponds to a high power consumption 510. Likewise, a high 60 FPS 508corresponds to a high data rate 512. Accordingly, input features 502 maydetermine the output measures 504, in term of the table matrix (see FIG.6) defined by the machine learning algorithm described below. Oneskilled in the relevant arts will appreciate that additional parametersmay be used to define input features and output measures.

At stage 308, power consumption of the secondary platform with anexpected performance level is predicted for a new application running onthe secondary platform based on the training set. For example, powerpredicting module 217 predicts power consumption for a new applicationrunning on the secondary platform. Based on this prediction, an optimalpower management policy may be deduced that minimizes the overall powerconsumption of the primary and secondary platforms. According to anembodiment, stage 308 may further include a training period 404 and aclassification period 406, as illustrated in FIG. 4.

According to an embodiment, in the training period 404, based on thetraining set collected on stage 306, a machine learning algorithm isused to recognize power consumption patterns with an expectedperformance level. One skilled in the relevant arts will appreciate thatany machine learning approaches such as decision tree learning, Bayesiannetwork, and clustering analysis can be utilized to make thepredictions.

FIG. 6 depicts a classification of the input features and outputmeasures, according to another embodiment of the present disclosure. Inthe training table illustrated in FIG. 6, input features are collectedduring a training period, with the corresponding output measures listedtherein as well. For example, utilization rate 602 and FPS 604 areclassified or categorized into low, med or high categories respectively.For example, a low utilization can be defined as 0-30%; a mediumutilization is 31-60%; and a high utilization is above 60%.

In another example, data rate between the first and second platforms canbe defined in ranges such as: D1=[10 20] mbps, while D2=[20 40] andD3=[40 60]. In still another example, power consumption of the primaryplatform P1=[140 160] mW; P2=[160 180] and P3=[180 200]. One skilled inthe relevant arts will appreciate that the examples described above arefor illustration, not for limitation purpose.

Notably, the same input features may generate different output measureswhich fall into different categories. As shown in FIG. 6, althoughentries 610 D2 and 612 D3 have same categories of utilization rate, FPSand power consumption, their corresponding data rate ranges may differ.For example, entry 610 may correspond to HD video which has anutilization of 77%, while entry 612 may correspond to Standard videowhich has an utilization of 63%. Although these two entries may havesimilar ranges of utilization rate, FPS or power consumption of theprimary platform, the data rates D2 or D3 may still fall withindifferent ranges.

In one embodiment, the categorization of the input features and outputmeasure into various classes defined with low, medium and high, or P1,P2 and P3 class labels for the corresponding attributes of utilizationrate, FPS or power consumption.

In one embodiment, based on the training table built during the trainingperiod, for a given new application, the data rate between the primaryand secondary platforms may be projected. The power consumption of thesecondary platform may be a function of the data rate. According toanother embodiment, based on the classification, the upcoming powerconsumption can be predicated for the given performance level, thus theoptimal operating frequency for the primary and secondary platform.

According to an embodiment of the present disclosure, when a new task orapplication is given, the upcoming power consumption and performancelevel may be predicted based on the classification noted above.Specifically, given the input features, the most likely output classlabel of output measures may be predicted. In an embodiment, a Maximum APosteriori (MAP) class may be identified, by assigning the maximum aposteriori class given data x and the prior class assignment to y andmaximizing the posterior probability of assigning class 1 to out featurey.

According an embodiment, in the classification period 406, an inputfeature y of the application running on the secondary platform can beclassified into class 1 based on the equation:

$\begin{matrix}{{y_{MAP} = {\arg \; {\max\limits_{l}\mspace{11mu} {{Prob}\left( {{y = {lx_{1}}},x_{2},\ldots \mspace{14mu},x_{n}} \right)}}}}\;} \\{= {\arg \; {\max\limits_{l}\mspace{14mu} \frac{{Prob}\; {\left( {x_{1},x_{2},\ldots \mspace{14mu},{{x_{n}y} = l}} \right) \cdot {{Prob}\left( {y = l} \right)}}}{{Prob}\; \left( {x_{1},x_{2},\ldots \mspace{14mu},x_{n}} \right)}}}} \\{= {\arg \; {\max\limits_{l}\mspace{14mu} {{{Prob}\left( {y = l} \right)} \cdot {\prod\limits_{j = 1}^{l}\; {{prob}\left( {{x_{j}y} = l} \right)}}}}}}\end{matrix}$

where x1 is a utilization rate, x2 is a frame frequency of the secondaryplatform, and y_(map) is a maximal probability of assigning class 1 toan output feature y.

Further, given the input feature x_(j), the possibility of assigning anoutput feature y to class 1 may be calculated as:

${{Prob}\left( {{x_{j}y} = l} \right)} = \frac{{{freq}\left( {x_{j},{y = l}} \right)} + \lambda}{{{freq}\left( {y = l} \right)} + {\lambda \; n}}$

where λ is a smoothing constant (>0), and n is the number of differentattributes of input feature x that have been observed.

For example, assuming input feature [Util, FPS]=[Med Med]=[x1, x2], thepower consumption of the secondary platform may be estimated via thefollowing equation:

$\begin{matrix}{{{Prob}\left( {{x\; 2} = {{{Med}y} = {P\; 3}}} \right)} = \frac{{{freq}\left( {{{x\; 2} = {Med}},{y = {P\; 3}}} \right)} + \lambda}{{{freq}\left( {y = {P\; 3}} \right)} + {\lambda \; n}}} \\{= \frac{0 + 1}{2 + {1 \cdot 3}}} \\{= \frac{1}{5}}\end{matrix}$

Thus, calculation of the probability of each hypothesis below generatesthe following results:

(1) For the hypothesis y=P3, Prob(y=P3)*Prob(x1=Med,x2=Med|y=P3)=⅜*½*⅕=0.038; since Prob(x1=Med|y=P3)=½, Prob(x2=Med|y=P3)=⅕

(2) For the hypothesis y=P2, Prob(y=P2)*Prob(x1=Med,x2=Med|y=P2)=4/8*4/4*3/4=0.375; since Prob(x1=Med|y=P2)=4/4,Prob(x2=Med|y=P2)=¾

(3) For the hypothesis y=P1, Prob(y=P1)*Prob(x1=Med,x2=Med|y=P1)=2/8*½*½=0.150, since Prob(x1=Med|y=P1)=½,Prob(x2=Med|y=P1)=½

Accordingly, higher value for the hypothesis y=P2, which corresponds toa prediction of power consumption as P2=[160 180] mW.

At stage 310, a power management policy may be adjusted with an optimalclock frequency of the secondary platform that minimizes a total powerconsumption of the primary platform and the secondary platform. A powermanagement policy may include several power management features that ahost or platform provides to adjust the trade-off between performanceand power consumption. For example, policy adjusting module 219 mayadjust the power management policy to minimize the total powerconsumption. Stage 310 may correspond to the power control period 408 asillustrated in FIG. 4.

In an embodiment, the secondary platform may change the clock frequency.For example, if high performance is needed for the secondary platform,the clock frequency may be changed from 100 MHz to 300 MHz.Alternatively, if low performance is needed for the secondary platform,the clock frequency may be changed from 300 MHz to 100 MHz.

In another embodiment, assuming prediction of power consumption Px=[p−p+] and data rate Dy=[d− d+], and power consumption of the secondaryplatform is estimated as P_(—)2^(nd)=[func(d−) func(d+)]. Thus, thechange of FPS is proportional to clock frequency changeFreq_new/Freq_current.

For example, if change the clock frequency to 200 MHz from 250 MHz @32fps, then, expected fps is 32*(200/250)=˜25 fps,

where 32 fps represents 31.3 ms for a frame, and whereactive_period=17.5 ms when Util=0.56.

In another example, if change clock frequency to 200 MHz from 250 MHz@p−=160 mW, then, expected p− is 160*(200/250)=128 mW.

Accordingly, in an embodiment, power consumption may be scaled based onFreq_new/Freq_current.

Therefore, according to an embodiment of the present disclosure, thepower management policy may be adjusted based on:

$\begin{matrix}{{DFS}_{new} = {\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left( {{Energy}_{primary} + {Energy}_{secondary}} \right)}}} \\{= {\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left\lbrack {\left( \frac{{p_{-} \cdot t_{p}} + {p_{+} \cdot t_{p}}}{2} \right) + \left( \frac{{{func}\; {\left( d_{-} \right) \cdot t_{s}}} + {{{func}\left( d_{+} \right)} \cdot t_{s}}}{2} \right)} \right\rbrack}}}\end{matrix}$

such that FPS_(secondary)>user_defined

wherein freq1 is a clock frequency of the primary platform; freq2 is aclock frequency of the secondary platform; t_(p) is an active period ofthe primary platform; t_(s) is an active period of the secondaryplatform, assuming prediction of a power consumption range Px=[p−, p+]and a data rate range Dy=[d−, d+] and the power consumption of thesecondary platform is estimated as P_(—)2^(nd)=[func(d−), func(d+)].

One skilled in the relevant arts will appreciate that stage 310 isoptional and the present disclosure may be practiced without thenecessity to adjust the power management policy. Furthermore, oneskilled in the relevant arts will appreciate that the aforementionedstages described in FIG. 3 could be executed in different combinationsand with varying degrees of parallelism.

It would be apparent to one of skill in the art that the presentdisclosure, as described above, can be implemented in many differentembodiments of software, hardware, firmware, and/or the entitiesillustrated in the figures. Any actual software code with thespecialized control of hardware to implement the present disclosure isnot limiting of the present disclosure. Thus, the operational behaviorof the present disclosure will be described with the understanding thatmodifications and variations of the embodiments are possible, and withinthe scope and spirit of the present disclosure.

IV. Example Computer Implementation

Various aspects of the present disclosure can be implemented bysoftware, firmware, hardware, or a combination thereof. FIG. 7illustrates an example computer system 700 in which the presentdisclosure, or portions thereof, can be implemented as computer-readablecode. For example, system 200 in FIG. 2 and the processes in FIGS. 3 and4 can be implemented in system 700. Various embodiments of thedisclosure are described in terms of this example computer system 700.After reading this description, it will become apparent to a personskilled in the relevant art how to implement the embodiments using othercomputer systems and/or computer architectures.

Computer system 700 includes one or more processors, such as processor704. Processor 704 can be a special purpose or a general purposeprocessor. Processor 704 is connected to a communication infrastructure706 (for example, a bus or network).

Computer system 700 also includes a main memory 708, preferably randomaccess memory (RAM), and may also include a secondary memory 710.Secondary memory 710 may include, for example, a hard disk drive 712, aremovable storage drive 714, and/or a memory stick. Removable storagedrive 714 may comprise a floppy disk drive, a magnetic tape drive, anoptical disk drive, a flash memory, or the like. The removable storagedrive 714 reads from and/or writes to a removable storage unit 715 in awell-known manner. Removable storage unit 715 may comprise a USB flashdata storage, magnetic tape, optical disk, etc. that is read by andwritten to by removable storage drive 714. As will be appreciated bypersons skilled in the relevant art(s), removable storage unit 715includes a computer usable storage medium having stored therein computersoftware and/or data.

In alternative implementations, secondary memory 710 may include othersimilar means for allowing computer programs or other instructions to beloaded into computer system 700. Such means may include, for example, aremovable storage unit 722 and an interface 720. Examples of such meansmay include a program cartridge and cartridge interface (such as thatfound in video game devices), a removable memory chip (such as an EPROM,or PROM) and associated socket, and other removable storage units suchas removable storage unit 722 and interfaces such as interface 720 thatallow software and data to be transferred from the removable storageunit 722 to computer system 700.

Computer system 700 may also include a communications interface 724.Communications interface 724 allows software and data to be transferredbetween computer system 700 and external devices. Communicationsinterface 724 may include a modem, a network interface (such as anEthernet card), a communications port, a PCMCIA slot and card, or thelike. Software and data transferred via communications interface 724 arein the form of signals that may be electronic, electromagnetic, optical,or other signals capable of being received by communications interface724. These signals are provided to communications interface 724 via acommunications path 726. Communications path 726 carries signals and maybe implemented using wire or cable, fiber optics, a phone line, acellular phone link, an RF link or other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage unit 715, removable storage unit 722, and a hard disk installedin hard disk drive 712. Signals carried over communications path 726 canalso embody the logic described herein. Computer program medium andcomputer usable medium can also refer to memories, such as main memory708 and secondary memory 710, which can be memory semiconductors (e.g.DRAMs, etc.). These computer program products are means for providingsoftware to computer system 700.

Computer programs (also called computer control logic) are stored inmain memory 708 and/or secondary memory 710. Computer programs may alsobe received via communications interface 724. Such computer programs,when executed, enable computer system 700 to implement the presentdisclosure as discussed herein. In particular, the computer programs,when executed, enable processor 704 to implement the processes of thepresent disclosure, such as the steps in the methods illustrated byflowcharts 200 of FIGS. 2 and 300 of FIG. 3, discussed above.Accordingly, such computer programs represent controllers of thecomputer system 700. Where the disclosure is implemented using software,the software may be stored in a computer program product and loaded intocomputer system 700 using removable storage drive 714, interface 720,hard drive 712 or communications interface 724.

The disclosure is also directed to computer program products comprisingsoftware stored on any computer useable medium. Such software, whenexecuted in one or more data processing device, causes a data processingdevice(s) to operate as described herein. Embodiments of the disclosureemploy any computer useable or readable medium, known now or in thefuture. Examples of computer useable mediums include, but are notlimited to, primary storage devices (e.g., any type of random accessmemory), secondary storage devices (e.g., hard drives, floppy disks, CDROMS, ZIP disks, tapes, magnetic storage devices, optical storagedevices, MEMS, nanotechnological storage device, etc.), andcommunication mediums (e.g., wired and wireless communications networks,local area networks, wide area networks, intranets, etc.).

V. Conclusion

It is to be appreciated that the Detailed Description section, and notthe Summary and Abstract sections, is intended to be used to interpretthe claims. The Summary and Abstract sections may set forth one or morebut not all exemplary embodiments of the present disclosure ascontemplated by the inventor(s), and thus, are not intended to limit thepresent disclosure and the appended claims in any way.

The present disclosure has been described above with the aid offunctional building blocks illustrating the implementation of specifiedfunctions and relationships thereof. The boundaries of these functionalbuilding blocks have been arbitrarily defined herein for the convenienceof the description. Alternate boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the disclosure that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent disclosure. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of the present disclosure should not be limited byany of the above-described exemplary embodiments, but should be definedonly in accordance with the following claims and their equivalents.

What is claimed is:
 1. A power management system, comprising: aprocessor to process modules; a memory, coupled to the processor,configured to store the modules including: a communication moduleconfigured to establish communication between a primary platform and asecondary platform; an application capturing module configured tocapture an application running on the secondary platforms; a collectingmodule configured to collect input features and output measures as atraining set for the application, wherein the input features arecollected through direct measurement and the output measures reflectcharacteristics of the application; and a power predicting moduleconfigured to predict power consumption of the secondary platform withan expected performance level for a new application running on thesecondary platform based on the training set.
 2. The system of claim 1,further comprising: a policy adjusting module configured to adjust apower management policy with an optimal clock frequency of the secondaryplatform that minimizes a total power consumption of the primaryplatform and the secondary platform.
 3. The system of claim 1, furthercomprising: a training set building module configured to build thetraining set based on a machine learning technique.
 4. The system ofclaim 1, wherein the primary platform is a mobile platform.
 5. Thesystem of claim 1, wherein the secondary platform is a wearable mobiledevice communicating with the primary platform.
 6. The system of claim1, wherein the input features include a utilization rate or a framefrequency.
 7. The system of claim 1, wherein the output measures includethe power consumption of the primary platform or a data rate of thesecondary platform.
 8. The system of claim 1, wherein the powerconsumption of the secondary platform is proportional to a data rate ofthe secondary platform.
 9. The system of claim 1, further comprising: afeature classification module configured to categorize a feature y ofthe secondary platform into a class 1 based on: $\begin{matrix}{{y_{MAP} = {\arg \; {\max\limits_{l}\mspace{11mu} {{Prob}\left( {{y = {lx_{1}}},x_{2},\ldots \mspace{14mu},x_{n}} \right)}}}}\;} \\{= {\arg \; {\max\limits_{l}\mspace{14mu} \frac{{Prob}\; {\left( {x_{1},x_{2},\ldots \mspace{14mu},{{x_{n}y} = l}} \right) \cdot {{Prob}\left( {y = l} \right)}}}{{Prob}\; \left( {x_{1},x_{2},\ldots \mspace{14mu},x_{n}} \right)}}}} \\{= {\arg \; {\max\limits_{l}\mspace{14mu} {{{Prob}\left( {y = l} \right)} \cdot {\prod\limits_{j = 1}^{l}\; {{prob}\left( {{x_{j}y} = l} \right)}}}}}}\end{matrix}$ wherein x1 is a utilization rate, x2 is a frame frequencyof the secondary platform, and y_(map) is a maximal probability ofassigning class 1 to an output feature y.
 10. The system of claim 1,wherein the power management policy is adjusted based on:$\begin{matrix}{{DFS}_{new} = {\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left( {{Energy}_{primary} + {Energy}_{secondary}} \right)}}} \\{= {\arg \; {\min\limits_{{{freq}\; 1},{{freq}\; 2}}\mspace{14mu} \left\lbrack {\left( \frac{{p_{-} \cdot t_{p}} + {p_{+} \cdot t_{p}}}{2} \right) + \left( \frac{{{func}\; {\left( d_{-} \right) \cdot t_{s}}} + {{{func}\left( d_{+} \right)} \cdot t_{s}}}{2} \right)} \right\rbrack}}}\end{matrix}$ such that FPS_(secondary)>user_defined wherein freq1 is aclock frequency of the primary platform; freq2 is a clock frequency ofthe secondary platform; t_(p) is an active period of the primaryplatform; t_(s) is an active period of the secondary platform, assumingprediction of a power consumption range Px=[p−, p+] and a data raterange Dy=[d−, d+] and the power consumption of the secondary platform isestimated as P_(—)2^(nd)=[func(d−), func(d+)].
 11. A computer-readablestorage device having instructions stored thereon, execution of which,by a computing device, causes the computing device to perform operationscomprising: establishing communication between a primary platform and asecondary platform; capturing an application running on the secondaryplatform; collecting input features and output measures as a trainingset for the application, wherein the input features are collectedthrough direct measurement and the output measures reflectcharacteristics of the application; and predicting power consumption ofthe secondary platform with an expected performance level for a newapplication running on the secondary platform based on the training set.12. The computer-readable storage device of claim 11, the operationsfurther comprising: adjusting a power management policy with an optimalclock frequency of the secondary platform that minimizes a total powerconsumption of the primary platform and the secondary platform.
 13. Thecomputer-readable storage device of claim 11, the operations furthercomprising: building the training set based on a machine learningtechnique.
 14. The computer-readable storage device of claim 11, whereinthe primary platform is a mobile platform.
 15. The computer-readablestorage device of claim 11, wherein the secondary platform is a wearablemobile device communicating with the primary platform.
 16. Thecomputer-readable storage device of claim 11, wherein the input featuresinclude a utilization rate or a frame frequency.
 17. Thecomputer-readable storage device of claim 11, wherein the outputmeasures include power consumption of the primary platform or a datarate of the secondary platform.
 18. The computer-readable storage deviceof claim 11, wherein the power consumption of the secondary platform isproportional to a data rate of the secondary platform.
 19. A method forsupervised power management, comprising: establishing communicationbetween a primary platform and a secondary platform; capturing anapplication running on the secondary platform; collecting input featuresand output measures as a training set for the application, wherein theinput features are collected through direct measurement and the outputmeasures reflect characteristics of the application; and predictingpower consumption of the secondary platform with an expected performancelevel for a new application running on the secondary platform based onthe training set.
 20. The method of claim 19, further comprising:adjusting a power management policy with an optimal clock frequency ofthe secondary platform that minimizes a total power consumption of theprimary platform and the secondary platform.